Modern three-dimensional imaging techniques, such as computerised tomography (CT), have the ability to produce volumetric representations of anatomy allowing users to examine acquired data retrospectively or under live screening from any plane and apply image processing techniques to achieve accurate viewing of individual structures.
Such three-dimensional techniques produce large three-dimensional volume datasets comprising a three-dimensional array of voxels each representing a property of a corresponding measurement volume. In the case of CT datasets, each voxel usually represents the attenuation of X-ray radiation by a respective, corresponding measurement volume.
It can be desired to identify particular anatomical regions or features from a volumetric dataset for a variety of different purposes.
For example, very large quantities of CT datasets or other volumetric imaging datasets are already in existence, for instance stored in patient or hospital databases. Usually data is stored together with metadata such as patient or measurement data. The patient or measurement data may identify the patient on whom the measurement was performed, may represent at least some of the measurement parameters, and may identify the region of the patient on which the measurement was performed. In some cases label data is stored with an imaging dataset, that labels particular features of the image represented by the dataset. However, the amount and quality of patient or measurement data, or other metadata, that is stored with each imaging dataset can vary widely. It can be difficult to determine what a particular imaging dataset represents, without skilled input from a radiologist or other medical personnel. It can also be difficult, or impossible, to effectively automatically search medical imaging databases for datasets representative of images of particular anatomical features, given the usual metadata that is provided with such datasets.
Another area in which it can be important to identify anatomical regions of an image relates to the use of scout images in CT imaging or other imaging modalities. When performing CT imaging, an initial set of imaging measurements is performed on a patient, often from a single angle or set of angles. The measurements usually comprise X-ray projection measurements on the patient at a fixed angular position of the X-ray source. Such initial measurements are often of relatively low power or resolution. The initial measurements are referred to as scout image measurements, and the resulting image can be referred to as a scout image and is similar to a convention X-ray image. The term scanogram can also be used to refer to the scout image. An operator typically examines the scout image to identify the position of a patient relative to the imaging apparatus, and identify the approximate position of particular anatomical features or regions. The operator then uses that information to set up the imaging apparatus for subsequent more accurate or higher dosage measurements of particular anatomical regions. Examination of the scout image by the operator is usually required to identify anatomical regions and no automatic procedure is provided. If the operator makes a mistake in examining the scout image then incorrect anatomical regions of the patient may subsequently be exposed to radiation.
In other examples, it is commonly desired to identify anatomical regions to enable subsequent analysis, or viewing, of data representative of particular anatomical features. A range of techniques has been developed to locate and identify particular anatomical features or regions or to automatically select particular parts of a medical imaging dataset for imaging or analysis.
Some known techniques locate anatomical regions or features using tissue and organ segmentation. In such techniques a thresholding and region growing approach is used to segment image data representative of the torso and separate it into various tissues, such as skin, subcutaneous fat, visceral fat, muscle, organs, skeleton, diaphragm and thoracic and abdominal cavities. According to such approaches each voxel is identified as representing for example, air, fat, muscle or organ, or skeleton based upon comparison of the Houndsfield Unit (HU) value for that voxel. The different types of voxels are then used in region growing procedures and different anatomical features are identified based upon the distribution of the different types of voxels.
In an alternative technique, body outline, bone equivalent and lung equivalent regions are detected by thresholding and connected component labelling. A crude anatomic region classification into thorax, pelvis or unknown is then performed based on analysis of the bone and lung segments in each slice of data. Patient position (supine, prone, or unknown) is also determined. Next, slices containing anatomic points are identified using any of a variety of different techniques chosen as suitable for the anatomic point of interest. Finally, based on interpolation within the map implied by the anatomic points, the pelvic and thoracic regions are further subdivided into their constituent regions and organs.
In another technique, anatomical features are located using axial slice classification based on machine learning techniques. Firstly, axial CT slices are rotated to a normalized state. Secondly, each slice is classified into one of a number of different classes (for example based on a set of features of the image data of each slice. Thirdly, dynamic programming is used to resolve inconsistencies in the ordering of the slice classes.
A further known technique is based on the analysis of a thresholded 2D projection of a skeleton. Horizontal lines through the binary 2D image are clustered based on the width of the interval defined by the most distal above-threshold points, and the proportion of above-threshold points lying within that interval. Clustered regions (contiguous sets of horizontal lines belonging to the same cluster) are then classified using a trained classifier into different skeletal regions.
The known automatic or semi-automatic techniques mentioned above for identifying anatomical regions or features are generally computationally costly and involve complex processing pipelines and algorithms. The techniques generally have some other aim, for example the detailed analysis of a particular anatomical feature, and the detection or estimation of anatomical regions is usually obtained as a by-product of that other aim. The known techniques mentioned above are not generally well suited to the rapid and computationally efficient determination of anatomical regions of three dimensional imaging datasets.
In the case of the other applications mentioned above for which identification of anatomical regions can be useful, such identification is usually performed manually by an operator, for example by an examination of a scout image or examination of stored images in a database.